Correction of technical bias in clinical microarray data improves concordance with known biological information

Aron Charles Eklund, Zoltan Imre Szallasi

    Research output: Contribution to journalJournal articleResearchpeer-review

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    Abstract

    The performance of gene expression microarrays has been well characterized using controlled reference samples, but the performance on clinical samples remains less clear. We identified sources of technical bias affecting many genes in concert, thus causing spurious correlations in clinical data sets and false associations between genes and clinical variables. We developed a method to correct for technical bias in clinical microarray data, which increased concordance with known biological relationships in multiple data sets.
    Original languageEnglish
    JournalGenome Biology
    Volume9
    Issue number2
    Pages (from-to)R26
    ISSN1465-6906
    DOIs
    Publication statusPublished - 2008

    Cite this

    Eklund, Aron Charles ; Szallasi, Zoltan Imre. / Correction of technical bias in clinical microarray data improves concordance with known biological information. In: Genome Biology. 2008 ; Vol. 9, No. 2. pp. R26.
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    abstract = "The performance of gene expression microarrays has been well characterized using controlled reference samples, but the performance on clinical samples remains less clear. We identified sources of technical bias affecting many genes in concert, thus causing spurious correlations in clinical data sets and false associations between genes and clinical variables. We developed a method to correct for technical bias in clinical microarray data, which increased concordance with known biological relationships in multiple data sets.",
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    Correction of technical bias in clinical microarray data improves concordance with known biological information. / Eklund, Aron Charles; Szallasi, Zoltan Imre.

    In: Genome Biology, Vol. 9, No. 2, 2008, p. R26.

    Research output: Contribution to journalJournal articleResearchpeer-review

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    AU - Szallasi, Zoltan Imre

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    N2 - The performance of gene expression microarrays has been well characterized using controlled reference samples, but the performance on clinical samples remains less clear. We identified sources of technical bias affecting many genes in concert, thus causing spurious correlations in clinical data sets and false associations between genes and clinical variables. We developed a method to correct for technical bias in clinical microarray data, which increased concordance with known biological relationships in multiple data sets.

    AB - The performance of gene expression microarrays has been well characterized using controlled reference samples, but the performance on clinical samples remains less clear. We identified sources of technical bias affecting many genes in concert, thus causing spurious correlations in clinical data sets and false associations between genes and clinical variables. We developed a method to correct for technical bias in clinical microarray data, which increased concordance with known biological relationships in multiple data sets.

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